Energy-Aware Explainable AI Framework for Sustainable Cloud Computing: A Novel Approach to Green Machine Learning with Real-Time Carbon Footprint Optimization

This research introduces a novel Energy-Aware Explainable AI (EA-XAI) framework that integrates carbon footprint optimization with interpretable machine learning for sustainable cloud computing environments. The proposed system achieves a remarkable 34.7% reduction in energy consumption while maintaining model accuracy at 94.2% and providing realtime explanations with SHAP values computed in$\mathbf{1 2. 3 m s}$average latency. The framework incorporates green computing principles through dynamic resource allocation algorithms that optimize carbon emissions (CO reduction of 2.1 tons/month), renewable energy utilization efficiency of 87.3%, and cost optimization achieving 41.6% reduction in operational expenses. The methodology addresses critical challenges in sustainable AI deployment by implementing novel carbon-aware scheduling algorithms, energy-efficient model compression techniques achieving 67.8% model size reduction, and transparent decision-making processes suitable for industrial applications requiring regulatory compliance and environmental sustainability certifications.